Forecasting non-Gaussian spatial along-wind pressure using wavelet kernel and multiplicative mixed kernel functions based LSSVM

被引：0
|
作者：
Chi E. [1 ]
Li C. [1 ]
Zheng X. [2 ]
机构：
[1] Department of Civil Engineering, Shanghai University, Shanghai
[2] Department of Structural Engineering, Tongji University, Shanghai
来源：
Zheng, Xiaofen | 1600年 / Chinese Vibration Engineering Society卷 / 36期
关键词：
Forecasting; Least squares support vector machines; Multiplicative mixed kernel functions; Non-Gaussian spatial along-wind pressure; Particle swarm optimization; Wavelet kernel functions;
D O I：
10.13465/j.cnki.jvs.2017.09.018
中图分类号：
学科分类号：
摘要：
Here, Marr wavelet kernel-based least squares support vector machines (LSSVM) referred to as Marr-LSSVM, was proposed to predict along-wind non-Gaussian spatial wind pressure. Through multiplication operation of the conventional radial basis function (RBF) kernel and polynomial kernel, Poly*RBF-LSSVM was then proposed, it was called the multiplicative mixed kernel MK-LSSVM. By using the particle swarm optimization (PSO) algorithm, optimizations were implemented for penalty parameters, kernel parameters, weights, and scale factors of Marr-LSSVM, conventional single kernel CSK-LSSVM, and MK-LSSVM, thus the non-Gaussian spatial wind pressure forecasting algorithms were built based on intelligent optimization. The simulated along-wind pressures at 30 m and 50 m were taken as input samples, the wind pressure at 40 m was then predicted using the proposed algorithms. The numerical analyses demonstrated that Marr-LSSVM and MK-LSSVM can provide an obvious higher performance to predict the non-Gaussian spatial wind pressure than CSK-LSSVM can. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：116 / 121
页数：5
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